Data sets generated in computing applications—e.g., those used in business intelligence and other applications—have been growing in size and complexity for some time. At the same time, more and more data of a sensitive nature is communicated over networks and/or stored or computed/processed using remote servers. By way of example, the use of payment cards for a broad spectrum of cashless transactions has become ubiquitous in the current economy, accounting for hundreds of billions of dollars in transactions per year. MasterCard International Incorporated, one example of a payment card network operator, processes millions of transactions per hour across roughly 230 countries. Aspects involved with the use of payment cards typically include the authentication of the payor/consumer using the payment card, as well as the authorization of the transaction based upon the availability of monies in the payor's/consumer's bank account. During this cashless transaction process, a large amount of transaction data, some of which is considered sensitive financial data, is generated and collected, often rapidly. Other examples of environments or applications involving data with a high volume, variety, velocity of generation/processing/transport, variability, and complexity generally referred to as “big data”—include medical records, retail, government databases, and the like.
In these types of applications and others, in which sensitive information is generated, collected, processed, accessible to various clients, and so on, it may frequently be desirable for such data to be stored, transferred, processed, etc., in a secure system with secure protocols. One example of a security benchmark for such protocols exists in the payment card setting and is known as the Payment Card Industry (“PCI”) Data Security Standard. The PCI Standard was created to increase controls and security protocols used in connection with cardholder information (e.g., to reduce credit card fraud). Other examples of security benchmarks and the like may include security requirements associated with the Health Insurance Portability and Accountability Act (“HIPAA”). Many government database and computing applications deal in sensitive information as well, and thus in various instances may be required to satisfy some minimum requirements related to security and encryption of data.
Nevertheless, traditional computing systems used in large-scale (e.g., big data) applications do not meet typical security protocols for, e.g., the above-mentioned types of applications. By way of illustration, HADOOP® networks have been designed and implemented to provide parallel computing for big data applications such as social media, other business intelligence applications, and the like, in which the data grows exponentially and tends to be difficult to timely (i.e., rapidly) collect in a structured manner. While the parallel, speedy, and scalable nature of HADOOP networks can be useful for aggregating and organizing large data sets, these types of networks often lack sufficient security measures to be amenable to applications such as those described above, wherein sensitive data is at issue, thus requiring protection and security measures to be implemented. Because existing large-scale computing (e.g., HADOOP-like) networks designed for big-data applications do not provide for sufficient security and/or encryption capabilities, secure networks are currently implemented using architectures that are not ideal for big data and like applications.